• Archives of Metallurgy and Materials
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• v.64 no.3
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• pp.1175-1182
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• 2019

In this study, we present a new method for obtaining the parameters of the Johnson-Mehl-Avrami-Kolmogorov equation for dynamic recrystallization grain size. The method consists of finite-element analysis and optimization techniques. An optimization tool iteratively minimizes the error between experimental values and corresponding finite-element solutions. Isothermal backward extrusion of the AA6060 aluminum alloy was used to acquire the main parameters of the equation for predicting DRX grain size. We compared grain sizes predicted using optimized and reference parameters with experimental values from the literature and found better agreement when the optimized parameters were applied.

• Structural Engineering and Mechanics
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• v.56 no.6
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• pp.959-982
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• 2015

In this study, a non-stationary random earthquake Clough-Penzien model is used to describe earthquake ground motion. Using stochastic direct integration in combination with an equivalent linear method, a solution is established to describe the non-stationary response of lead-rubber bearing (LRB) system to a stochastic earthquake. Two parameters are used to develop an optimization method for bearing design: the post-yielding stiffness and the normalized yield strength of the isolation bearing. Using the minimization of the maximum energy response level of the upper structure subjected to an earthquake as an objective function, and with the constraints that the bearing failure probability is no more than 5% and the second shape factor of the bearing is less than 5, a calculation method for the two optimal design parameters is presented. In this optimization process, the radial basis function (RBF) response surface was applied, instead of the implicit objective function and constraints, and a sequential quadratic programming (SQP) algorithm was used to solve the optimization problems. By considering the uncertainties of the structural parameters and seismic ground motion input parameters for the optimization of the bearing design, convex set models (such as the interval model and ellipsoidal model) are used to describe the uncertainty parameters. Subsequently, the optimal bearing design parameters were expanded at their median values into first-order Taylor series expansions, and then, the Lagrange multipliers method was used to determine the upper and lower boundaries of the parameters. Moreover, using a calculation example, the impacts of site soil parameters, such as input peak ground acceleration, bearing diameter and rubber shore hardness on the optimization parameters, are investigated.

• Journal of Power System Engineering
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• v.21 no.3
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• pp.66-73
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• 2017

In order to identify the design parameters of a hydraulic engine mount with a nonlinear characteristics, an experimental method has been used generally. The method takes a considerable time and expense because of preparing an experimental apparatus, conducting a test, and analyzing results. Therefore, this paper presents a simple method to identify the design parameters of a cylindrical hydraulic engine mount, and optimize the design parameters. The physical model and mathematical equations of the mount were derived, and values of the design parameters of the mount were identified by optimization method with minimizing difference between the analytical results with the equations and the experimental results. This method is more simpler than the conventional experiment method and identify successfully the design parameters. In addition, the technique can optimize the design parameters of the mount to improves the isolation performance of the mount.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.11 no.5
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• pp.347-352
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• 1998

In this paper, the extraction and optimization of SPICE parameters on SONOSFET for NVSM circuit design were discussed. SONOSFET devices with different channel widths and lengths were fabricated using conventional 1.2 um n-well CMOS process. And, electric properties for dc parameters and capacitance parameters were measured on wafer. SPICE parameters for the SONOSFET were extracted from the UC Berkeley level 3 model for the MOSFET. And, local optimization of Ids-Vgs curves has carried out in the bias region of subthreshold, linear, saturation respectively. Finally, the extracted SPICE parameters were optimized globally by comparing drain current (Ids), output conductance(gds), transconductance(gm) curves with theoretical curves in whole region of bias conditions. It is shown that the conventional model for the MOSFET can be applied to the SONOSFET modeling except sidewalk effect.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.1
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• pp.114-120
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• 2010

Optimization of the turning process has been concentrated on the selection of the optimal cutting parameters, such as cutting speed, feed rate and depth of cut. However, optimization of the cutting parameters does not necessarily guarantee the maximum profit. For the maximization of the profit, parameters other than cutting parameters have to be taken care of. In this study, 8 price-related parameters were considered to maximize the profit of the product. Regression equations obtained from RSM technique to relate the cutting parameters and maximum cutting volume with a given insert were used. The experiments with four combinations of cutting inserts and material were executed to compare the results that made the profit and cutting volume maximized. The results showed that the cutting parameters for volume and profit maximization were totally different. Contrary to our intuition, global optimization was achieved when the number of inserts change was larger than those for volume maximization. It is attributed to the faster cutting velocity, which decreases processing time and increasing the number of tool used and the total tool changing time.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.2
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• pp.128-133
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• 2010

This paper presents a method for estimating the parameters of dynamic models for induction motor dominating loads. Using particle swarm optimization, the method finds the adequate set of parameters that best fit the sampling data from the measurement for a period of time, minimizing the error of the outputs, active and reactive power demands and satisfying the steady-state error criterion.

• The KIPS Transactions:PartB
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• v.14B no.5
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• pp.377-382
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• 2007

This paper proposes a training algorithm for global optimization of the parameters of radial basis function networks. Since conventional training algorithms usually perform only local optimization, the performance of the network is limited and the final network significantly depends on the initial network parameters. The proposed hybrid simulated annealing algorithm performs global optimization of the network parameters by combining global search capability of simulated annealing and local optimization capability of gradient-based algorithms. Via experiments for function approximation problems, we demonstrate that the proposed algorithm can find networks showing better training and test performance and reduce effects of the initial network parameters on the final results.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2001.10a
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• pp.106-109
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• 2001

Process parameters of roller levelling process are intermesh of each roller, roller angle, roller arrangement and shape of rollers. Experimental optimization of these process parameters is very troublesome because of difficulties in evaluating the straightness of pipes to be levelled quantitatively. Finite element method can be a very efficient way to evaluate the straightness of the pipes and therefore to optimize the process. This paper is concerned with simulation and optimization of a roller levelling process. Process parameters of a 14-roller levller for aluminum T9 pipes are optimized with finite element method and Taguchi method. Parameters of significance in roller levelling process and their optimum are obtained.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.8
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• pp.344-351
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• 2002

The proposed immune algorithm has an uncomplicated structure and memory-cell mechanism as the optimization algorithm which imitates the principle of humoral immune response. We use the proposed algorithm to solve parameter optimization problems. Up to now, the applications of immune algorithm have been optimization problems with non-varying system parameters. Therefore the usefulness of memory-cell mechanism in immune algorithm is without. This paper proposes the immune algorithm using a memory-cell mechanism which can be the application of system with nonlinear varying parameters. To verified performance of the proposed immune algorithm, the speed control of nonlinear DC motor are performed. The results of Computer simulations represent that the proposed immune algorithm shows a fast convergence speed and a good control performances under the varying system parameters.

• Journal of applied mathematics & informatics
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• v.26 no.1_2
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• pp.355-364
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• 2008

Under pathological stress stimuli, dynamics of a biological system can be changed by alteration of several components such as functional proteins, ultimately leading to disease state. These dynamics in disease state can be modeled using differential equations in which kinetic or system parameters can be obtained from experimental data. One of the most effective ways to restore a particular disease state of biology system (i.e., cell, organ and organism) into the normal state makes optimization of the altered components usually represented by system parameters in the differential equations. There has been no such approach as far as we know. Here we show this approach with a cardiac hypertrophy model in which we obtain the existence of the optimal parameters and construct an optimal system which can be used to find the optimal parameters.